title: Revisiting comorbidities in gout: a cluster analysis.
abstract: OBJECTIVE
  The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout.
  
  
  METHODS
  This was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identifying homogeneous subgroups of patients. Variables used in both analyses were hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders and cancer.
  
  
  RESULTS
  Comorbidities were common in this large cohort of patients with gout. Abdominal obesity, hypertension, metabolic syndrome and dyslipidaemia increased with gout duration, even after adjustment for age and sex. Five clusters (C1-C5) were found. Cluster C1 (n=332, 12%) consisted of patients with isolated gout and few comorbidities. In C2 (n=483, 17%), all patients were obese, with a high prevalence of hypertension. C3 (n=664, 24%) had the greatest proportion of patients with type 2 diabetes (75%). In C4 (n=782, 28%), almost all patients presented with dyslipidaemia (98%). Finally, C5 (n=502, 18%) consisted of almost all patients with a history of cardiovascular disease and renal failure, with a high rate of patients receiving diuretics.
  
  
  CONCLUSIONS
  Cluster analysis of comorbidities in gout allowed us to identify five different clinical phenotypes, which may reflect different pathophysiological processes in gout.
authors.affiliation: Université Paris 7, UFR médicale, Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisière, Fédération de Rhumatologie, Paris, Cedex 10, France.
authors.forename: Pascal
authors.initials: P
authors.lastname: Richette
authors.affiliation: Orgamétrie Biostatistiques, Roubaix, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Clerson
authors.affiliation: Ménarini, Rungis, France.
authors.forename: Laure
authors.initials: L
authors.lastname: Périssin
authors.affiliation: Service de Rhumatologie, Université de Lille 2, Hôpital Roger-Salengro, CHRU de Lille, Lille, France.
authors.forename: René-Marc
authors.initials: RM
authors.lastname: Flipo
authors.affiliation: Université Paris 7, UFR médicale, Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisière, Fédération de Rhumatologie, Paris, Cedex 10, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Bardin
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D015331:Cohort Studies; D015897:Comorbidity; D003327:Coronary Disease / Q000453:epidemiology*; D003920:Diabetes Mellitus / Q000453:epidemiology*; D050171:Dyslipidemias / Q000453:epidemiology*; D005260:Female; D005602:France / Q000453:epidemiology; D006073:Gout / Q000453:epidemiology*; D006333:Heart Failure / Q000453:epidemiology*; D006801:Humans; D006973:Hypertension / Q000453:epidemiology*; D008107:Liver Diseases / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000453:epidemiology*; D009765:Obesity / Q000453:epidemiology*; D015995:Prevalence; D051437:Renal Insufficiency / Q000453:epidemiology*
keywords: Cardiovascular Disease; Epidemiology; Gout